The purpose of this project is to extend our current work on the development of decision-analytic policy models in cancer to quantify the impact of disparities in screening, follow-up, and treatment in terms of its impact on cancer-related outcomes. We will initially develop a generalizable model that will serve as a framework for incorporating disparities in decision-analytic models. We will focus on disparities in cancer risk, cancer screening participation rates, treatment patterns, and their interactions. We will evaluate several hypothetical situations to highlight those areas where disparities are most concerning. In addition, we will leverage the work that we have already done to develop cohort-specific simulation models to address socially relevant policy questions that disproportionately affect certain segments of the population. We will conduct analyses for questions pertaining to three major cancers: colorectal, cervical and breast. The models will be evaluated as Monte Carlo simulations (one person at a time), which provide a substantial amount of flexibility in terms of keeping track of person-specific variables. The colorectal cancer and cervical cancer models simulate the evolution from a normal cervix or normal colonic epithelium to precancerous lesions (i.e., adenomatous polyps for the former model, cervical lesions for the latter) to malignancy. Cancer diagnosis occurs when a screening test is applied to a person with undiagnosed malignancy (based on the test sensitivity), or if symptoms occur that lead to a correct diagnosis (symptom-diagnosed cancer). Once a cancer is detected and staged, details of specific treatment strategies can be incorporated that affect cancer-specific mortality. The cancer-specific models will be used to analyze the effects of important contributors to disparities and how they relate to cancer control.